Lulu Dong1, 2, Lin Li1, 2, HongChao Ma3 and YeLing Liang1, 2, 1The State Key Laboratory of Tibetan Intelligent Information Processing and Application, China, 2Qinghai Normal University, China, 3Beijing Language and Culture University, China
Automated Essay Scoring (AES) aims to assign a proper score to an essay written by a given prompt, which is a significant application of Natural Language Processing (NLP) in the education area. In this work, we focus on solving the Chinese AES problem by Pre-trained Language Models (PLMs) including state-of-the-art PLMs BERT and ERNIE. A Chinese essay dataset has been built up in this work, by which we conduct extensive AES experiments. Our PLMs-based AES models acquire 68.70% in Quadratic Weighted Kappa (QWK), which outperform classic feature-based linear regression AES model. The results show that our methods effectively alleviate the dependence on manual features and improve the portability of AES models. Furthermore, we acquire well-performed AES models with a limited scale of the dataset, which solves the lack of datasets in Chinese AES.
Chinese Automated Essay Scoring, Neural Network, Pre-trained Language Model, Quadratic Weighted Kappa.